LA-Sign: Looped Transformers with Geometry-aware Alignment for Skeleton-based Sign Language Recognition

• URL: http://arxiv.org/abs/2603.29057v1
• Date: Mon, 30 Mar 2026 22:49:42 GMT
• Title: LA-Sign: Looped Transformers with Geometry-aware Alignment for Skeleton-based Sign Language Recognition
• Abstract summary: LA-Sign is a looped transformer framework with geometry-aware alignment for sign language recognition.<n>We show that LA-Sign achieves state-of-the-art results while using fewer unique layers.
• Score: 47.469519895247366
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Skeleton-based isolated sign language recognition (ISLR) demands fine-grained understanding of articulated motion across multiple spatial scales, from subtle finger movements to global body dynamics. Existing approaches typically rely on deep feed-forward architectures, which increase model capacity but lack mechanisms for recurrent refinement and structured representation. We propose LA-Sign, a looped transformer framework with geometry-aware alignment for ISLR. Instead of stacking deeper layers, LA-Sign derives its depth from recurrence, repeatedly revisiting latent representations to progressively refine motion understanding under shared parameters. To further regularise this refinement process, we present a geometry-aware contrastive objective that projects skeletal and textual features into an adaptive hyperbolic space, encouraging multi-scale semantic organisation. We study three looping designs and multiple geometric manifolds, demonstrating that encoder-decoder looping combined with adaptive Poincare alignment yields the strongest performance. Extensive experiments on WLASL and MSASL benchmarks show that LA-Sign achieves state-of-the-art results while using fewer unique layers, highlighting the effectiveness of recurrent latent refinement and geometry-aware representation learning for sign language recognition.

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